https://github.com/jokoum/stochastic-gradient-trees-python

Stochastic Gradient Trees implementation in Python
https://github.com/jokoum/stochastic-gradient-trees-python

classification-algorithm machine-learning python regression-algorithms scikit-learn sklearn stochastic-gradient tree-algorithms tree-classifiers tree-regressor

Last synced: 4 months ago
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Stochastic Gradient Trees implementation in Python

• Host: GitHub
• URL: https://github.com/jokoum/stochastic-gradient-trees-python
• Owner: JoKoum
• License: mit
• Created: 2021-10-18T19:15:20.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2022-11-18T18:15:28.000Z (over 2 years ago)
• Last Synced: 2025-02-17T05:46:47.196Z (4 months ago)
• Topics: classification-algorithm, machine-learning, python, regression-algorithms, scikit-learn, sklearn, stochastic-gradient, tree-algorithms, tree-classifiers, tree-regressor
• Language: Python
• Homepage: https://pypi.org/project/pysgt/
• Size: 85 KB
• Stars: 2
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
Stochastic Gradient Trees - Python

=========================

[Stochastic Gradient Trees](https://arxiv.org/abs/1901.07777)[^1] by Henry Gouk, Bernhard Pfahringer, and Eibe Frank implementation in Python. Based on the [parer's accompanied repository](https://github.com/henrygouk/stochastic-gradient-trees) code.

### Python Version 3.7 or later

### Used Python libraries:

* numpy>=1.20.2

* scipy>=1.6.2

* pandas>=1.3.3

* scikit-learn>=0.24.2

### Usage:

```python

    from pysgt.StochasticGradientTree import StochasticGradientTreeClassifier

    from sklearn.model_selection import train_test_split

    from sklearn.datasets import load_breast_cancer

    from sklearn.metrics import confusion_matrix, accuracy_score, log_loss

    def train(X, y):

        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.34)

        

        tree = StochasticGradientTreeClassifier()

        tree.fit(X_train, y_train)

    

        y_pred = tree.predict(X_test)

        proba = tree.predict_proba(X_test)        

        acc_test = accuracy_score(y_test, y_pred)

        print(confusion_matrix(y_test, y_pred))

        print('Acc test: ', acc_test)

        print('Cross entropy loss: ', log_loss(y_test, proba))

        return tree, acc_test

    if __name__ == "__main__":

        breast = load_breast_cancer(as_frame=True)

        X = breast.frame.copy()

        y = breast.frame.target

        

        X.drop(['target'], axis=1, inplace=True) 

        tree, _ = train(X, y)

```

### Binary classification example:

    python classification_breast.py

### Multiclass classification (using the [One-vs-the-rest](https://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsRestClassifier.html) multiclass strategy):

    python classification_iris.py

### Regression example:

    python regression_diabetes.py

[^1]: Gouk, H., Pfahringer, B., and Frank, E. Stochastic gradient trees. In Proceedings of The Eleventh Asian Conference on Machine Learning, volume 101 of Proceedings of Machine Learning Research, pp. 1094–1109. PMLR, 2019.